An LSM-Based Tuple Compaction Framework for Apache Asterixdb (Extended Version)
Total Page：16
File Type：pdf, Size：1020Kb
An LSM-based Tuple Compaction Framework for Apache AsterixDB (Extended Version) Wail Y. Alkowaileet Sattam Alsubaiee Michael J. Carey University of California, Irvine King Abdulaziz City for University of California, Irvine Irvine, CA Science and Technology Irvine, CA [email protected] Riyadh, Saudi Arabia [email protected] [email protected] ABSTRACT record embeds metadata that describes its structure and Document database systems store self-describing semi- values). The flexibility of the self-describing data model structured records, such as JSON, \as-is" without requiring provided by NoSQL systems attracts applications where the the users to pre-define a schema. This provides users with schema can change in the future by adding, removing, or the flexibility to change the structure of incoming records even changing the type of one or more values without tak- without worrying about taking the system offline or hin- ing the system offline or slowing down the running queries. dering the performance of currently running queries. How- The flexibility provided in document store systems over ever, the flexibility of such systems does not free. The large the rigidity of the schemas in Relational Database Manage- amount of redundancy in the records can introduce an un- ment Systems (RDBMSs) does not come without a cost. For necessary storage overhead and impact query performance. instance, storing a boolean value for a field named hasChil- Our focus in this paper is to address the storage over- dren, which takes roughly one byte to store in an RDBMS, head issue by introducing a tuple compactor framework that can take a NoSQL DBMS an order of magnitude more bytes infers and extracts the schema from self-describing semi- to store. Defining a schema prior to ingesting the data can structured records during the data ingestion. As many alleviate the storage overhead, as the schema is then stored prominent document stores, such as MongoDB and Couch- in the system's catalog and not in each record. However, base, adopt Log Structured Merge (LSM) trees in their stor- defining a schema defies the purpose of schema-less DBMSs, age engines, our framework exploits LSM lifecycle events which allow adding, removing or changing the types of the to piggyback the schema inference and extraction opera- fields without manually altering the schema [18, 23]. From tions. We have implemented and empirically evaluated our a user perspective, declaring a schema requires thorough a approach to measure its impact on storage, data ingestion, priori understanding of the dataset's fields and their types. and query performance in the context of Apache AsterixDB. Let us consider a scenario where a data scientist wants to ingest and analyze a large volume of semi-structured data PVLDB Reference Format: from a new external data source without prior knowledge of Wail Y. Alkowaileet, Sattam Alsubaiee and Michael J. Carey. An its structure. Our data scientist starts by acquiring a few LSM-based Tuple Compaction Framework in Apache AsterixDB. PVLDB, 13(9): xxxx-yyyy, 2020. instances from the data source and tries to analyze their DOI: https://doi.org/10.14778/3397230.3397236 structures; she then builds a schema according to the ac- quired sample. After ingesting a few data instances, our data scientist discovers that some fields can have more than 1. INTRODUCTION one type, which was not captured in her initial sample. As a Self-describing semi-structured data formats like JSON result, she stops the ingestion process, alters the schema to have become the de facto format for storing and sharing accommodate the irregularities in the types of those fields, information as developers moving away from the rigidity and then reinitiates the data ingestion process. In this case, of schemas in the relational model. Consequently, NoSQL our data scientist has to continuously monitor the system arXiv:1910.08185v2 [cs.DB] 11 May 2020 Database Management Systems (DBMSs) have emerged as and alter the schema if necessary, which may result in taking popular solutions for storing, indexing, and querying self- the system offline or stopping the ingestion of new records. describing semi-structured data. In document store systems Having an automated mechanism to infer and consolidate such as MongoDB [11] and Couchbase [10], users are not the schema information for the ingested records without los- required to define a schema before loading or ingesting their ing the flexibility and the experience of schema-less stores data since each data instance is self-describing (i.e., each would clearly be desirable. In this work, we address the problem of the storage over- head in document stores by introducing a framework that in- This work is licensed under the Creative Commons Attribution- fers and compacts the schema information for semi-structured NonCommercial-NoDerivatives 4.0 International License. To view a copy data during the ingestion process. Our design utilizes the of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For lifecycle events of Log Structured Merge (LSM) tree [31] any use beyond those covered by this license, obtain permission by emailing [email protected]. Copyright is held by the owner/author(s). Publication rights based storage engines, which are used in many prominent licensed to the VLDB Endowment. document store systems [10, 11] including Apache Aster- Proceedings of the VLDB Endowment, Vol. 13, No. 9 ixDB [19]. In LSM-backed engines, records are first accu- ISSN 2150-8097. mulated in memory (LSM in-memory component) and then DOI: https://doi.org/10.14778/3397230.3397236 1 subsequently written sequentially to disk (flush operation) of types int, string and a multiset of DependentType, re- in a single batch (LSM on-disk component). Our framework spectively. The symbol \?" indicates that a field is optional. takes the opportunity provided by LSM flush operations to Note that we defined the type EmployeeType as open, where extract and strip the metadata from each record and con- data instances of this type can have additional undeclared struct a schema for each flushed LSM component. We have fields. On the other hand, we define the DependentType as implemented and empirically evaluated our framework to closed, where data instances can only have declared fields. measure its impact on the storage overhead, data ingestion In both the open and closed datatypes, AsterixDB does not rate and query performance in the context of AsterixDB. permit data instances that do not have values for the spec- Our main contributions could be summarized as follows: ified non-optional fields. Finally, in this example, we create a dataset Employee of the type EmployeeType and specify • We propose a mechanism that utilizes the LSM work- its id field as the primary key. flow to infer and compact the schema for NoSQL sys- tems records during flush operations. Moreover, we detail the steps required for distributed query process- CREATE TYPE DependentType CREATE TYPE EmployeeType AS OPEN f ing using the inferred schema. AS CLOSED f name: string , id: int , • We introduce a non-recursive physical data layout that age: int name: string , separates data values from their metadata, which al- g; dependents:ffDependentTypegg? lows us to infer and compact the schema efficiently for g; nested data. • We introduce page-level compression in AsterixDB. CREATE DATASET Employee(EmployeeType) PRIMARY KEY id; This is a similar solution to these adopted by other Figure 1: Defining Employee type and dataset in ADM NoSQL DBMSs to reduce the storage overhead of self- describing records. To query the data stored in AsterixDB, users can submit • We evaluate the feasibility of our design, prototyped their queries written in SQL++ [24, 32], a SQL-inspired using AsterixDB, to ingest and query a variety of large declarative query language for semi-structured data. Fig- semi-structured datasets. We compare our \semantic" ure 2 shows an example of a SQL++ aggregate query posed approach of reducing the storage overhead to the \syn- against the dataset declared in Figure 1. tactic" approach of compression. The remainder of this paper is structured as follows: Sec- SELECT VALUE nameGroup FROM Employee AS emp tion 2.1 provides a preliminary review of the AsterixDB ar- GROUP BY emp . name GROUP AS nameGroup chitecture and our implementation for page-level compres- Figure 2: An example of a SQL++ query sion. Section 3.1 details the design and implementation of our tuple compaction framework in AsterixDB. Section 4 presents an experimental evaluation of the proposed frame- 2.2 Storage and Data Ingestion work. Section 5 discusses related work on utilizing the LSM In an AsterixDB cluster, each worker node (Node Con- lifecycle and on schema inference for semi-structured data. troller, or NC for short) is controlled by a Cluster Controller Finally, Section 6 presents our conclusions and discusses po- (CC) that manages the cluster's topology and performs rou- tential future directions for our work. tine checks on the NCs. Figure 3 shows an AsterixDB clus- ter of three NCs, each of which has two data partitions that 2. APACHE ASTERIXDB OVERVIEW hold data on two separate storage devices. Data partitions In this paper, we use Apache AsterixDB to prototype our in the same NC (e.g., Partition 0 and Partition 1 in NC0) tuple compactor framework. AsterixDB is a parallel semi- share the same buffer cache and memory budget for LSM in- structured Big Data Management System (BDMS) which memory components; however, each partition manages the runs on large, shared-nothing, commodity computing clus- data stored in its storage device independently. In this ex- ters. To prepare the reader, we give a brief overview of As- ample, NC0 also acts as a metadata node, which stores and terixDB [18, 23] and its query execution engine Hyracks [21].